{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## This notebook will help you train a vanilla Point-Cloud AE with the basic architecture we used in our paper.\n",
    "    (it assumes latent_3d_points is in the PYTHONPATH and the structural losses have been compiled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os.path as osp\n",
    "\n",
    "from latent_3d_points.src.ae_templates import mlp_architecture_ala_iclr_18, default_train_params\n",
    "from latent_3d_points.src.autoencoder import Configuration as Conf\n",
    "from latent_3d_points.src.point_net_ae import PointNetAutoEncoder\n",
    "\n",
    "from latent_3d_points.src.in_out import snc_category_to_synth_id, create_dir, PointCloudDataSet, \\\n",
    "                                        load_all_point_clouds_under_folder\n",
    "\n",
    "from latent_3d_points.src.tf_utils import reset_tf_graph\n",
    "from latent_3d_points.src.general_utils import plot_3d_point_cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Basic Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give me the class name (e.g. \"chair\"): chair\n"
     ]
    }
   ],
   "source": [
    "top_out_dir = '../data/'          # Use to save Neural-Net check-points etc.\n",
    "top_in_dir = '../data/shape_net_core_uniform_samples_2048/' # Top-dir of where point-clouds are stored.\n",
    "\n",
    "experiment_name = 'single_class_ae'\n",
    "n_pc_points = 2048                # Number of points per model.\n",
    "bneck_size = 128                  # Bottleneck-AE size\n",
    "ae_loss = 'chamfer'                   # Loss to optimize: 'emd' or 'chamfer'\n",
    "class_name = raw_input('Give me the class name (e.g. \"chair\"): ').lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Point-Clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6778 pclouds were loaded. They belong in 1 shape-classes.\n"
     ]
    }
   ],
   "source": [
    "syn_id = snc_category_to_synth_id()[class_name]\n",
    "class_dir = osp.join(top_in_dir , syn_id)\n",
    "all_pc_data = load_all_point_clouds_under_folder(class_dir, n_threads=8, file_ending='.ply', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default training parameters (some of which are listed beloq). For more details please print the configuration object.\n",
    "\n",
    "    'batch_size': 50   \n",
    "    \n",
    "    'denoising': False     (# by default AE is not denoising)\n",
    "\n",
    "    'learning_rate': 0.0005\n",
    "\n",
    "    'z_rotate': False      (# randomly rotate models of each batch)\n",
    "    \n",
    "    'loss_display_step': 1 (# display loss at end of these many epochs)\n",
    "    'saver_step': 10       (# over how many epochs to save neural-network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_params = default_train_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder, decoder, enc_args, dec_args = mlp_architecture_ala_iclr_18(n_pc_points, bneck_size)\n",
    "train_dir = create_dir(osp.join(top_out_dir, experiment_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conf = Conf(n_input = [n_pc_points, 3],\n",
    "            loss = ae_loss,\n",
    "            training_epochs = train_params['training_epochs'],\n",
    "            batch_size = train_params['batch_size'],\n",
    "            denoising = train_params['denoising'],\n",
    "            learning_rate = train_params['learning_rate'],\n",
    "            train_dir = train_dir,\n",
    "            loss_display_step = train_params['loss_display_step'],\n",
    "            saver_step = train_params['saver_step'],\n",
    "            z_rotate = train_params['z_rotate'],\n",
    "            encoder = encoder,\n",
    "            decoder = decoder,\n",
    "            encoder_args = enc_args,\n",
    "            decoder_args = dec_args\n",
    "           )\n",
    "conf.experiment_name = experiment_name\n",
    "conf.held_out_step = 5   # How often to evaluate/print out loss on \n",
    "                         # held_out data (if they are provided in ae.train() ).\n",
    "conf.save(osp.join(train_dir, 'configuration'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you ran the above lines, you can reload a saved model like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "load_pre_trained_ae = False\n",
    "restore_epoch = 500\n",
    "if load_pre_trained_ae:\n",
    "    conf = Conf.load(train_dir + '/configuration')\n",
    "    reset_tf_graph()\n",
    "    ae = PointNetAutoEncoder(conf.experiment_name, conf)\n",
    "    ae.restore_model(conf.train_dir, epoch=restore_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build AE Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reset_tf_graph()\n",
    "ae = PointNetAutoEncoder(conf.experiment_name, conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the AE (save output to train_stats.txt) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "buf_size = 1 # Make 'training_stats' file to flush each output line regarding training.\n",
    "fout = open(osp.join(conf.train_dir, 'train_stats.txt'), 'a', buf_size)\n",
    "train_stats = ae.train(all_pc_data, conf, log_file=fout)\n",
    "fout.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a batch of reconstuctions and their latent-codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feed_pc, feed_model_names, _ = all_pc_data.next_batch(10)\n",
    "reconstructions = ae.reconstruct(feed_pc)[0]\n",
    "latent_codes = ae.transform(feed_pc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Use any plotting mechanism such as matplotlib to visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5JxNBEHIVCubOnZsrdtff309bWxvhcBhZliksLCSRSEzrsU+3hTfd5LVgvVNiWKZpHjXy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=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfd42897d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Anux9KKR2dzpjOfd0Dfx8HJpW0IJlks1Iovg/W1r59a5OPnNlPb/e2UlTTxgd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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcfd41f8d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 2\n",
    "plot_3d_point_cloud(reconstructions[i][:, 0], \n",
    "                    reconstructions[i][:, 1], \n",
    "                    reconstructions[i][:, 2], in_u_sphere=True);\n",
    "\n",
    "i = 4\n",
    "plot_3d_point_cloud(reconstructions[i][:, 0], \n",
    "                    reconstructions[i][:, 1], \n",
    "                    reconstructions[i][:, 2], in_u_sphere=True);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow1",
   "language": "python",
   "name": "tf1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
